IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine最新文献_第4页

Toward personalized pan-omic association analysis under complex structures and big data 走向复杂结构和大数据下的个性化泛经济关联分析

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359647

E. Xing

{"title":"Toward personalized pan-omic association analysis under complex structures and big data","authors":"E. Xing","doi":"10.1109/BIBM.2015.7359647","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359647","url":null,"abstract":"Dr. Eric Xing is a Professor of Machine Learning in the School of Computer Science at Carnegie Mellon University, and Director of the CMU/UPMC Center for Machine Learning and Health. His principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. He servers (or served) as an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He was a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, and the IBM Open Collaborative Research Award. He was the Program Chair of ICML 2014.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"24 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80504055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parallel machine learning approaches for reverse engineering genome-scale networks 基因组规模网络逆向工程的并行机器学习方法

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359646

S. Aluru

引用次数: 0

An image reconstruction model and hybrid algorithm for limited-angle projection data 有限角度投影数据的图像重建模型及混合算法

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-01-01 DOI: 10.1109/BIBM.2015.7359715

Hongxia Gao, Yinghao Luo, Kewei Chen, Ge Ma, Lixuan Wu

引用次数: 1

Analysis of the correlation between the ambulatory arterial stiffness index, circadian rhythm and TCM syndrome differentiation in patients with essential hypertension 原发性高血压患者动态动脉僵硬指数、昼夜节律与中医辨证的相关性分析

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2014-01-01 DOI: 10.1109/BIBM.2014.6999357

H. Ye, Shuai Mao, Juan Chen, Qin-juan Wu, Yunfei Wang

引用次数: 0

Yin and Yang of reciprocally scale-free biological networks between disease genes and death genes 阴阳在疾病基因和死亡基因之间相互无标度的生物网络

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2013-12-01 DOI: 10.1109/BIBM.2013.6732449

Ju Han Kim

{"title":"Yin and Yang of reciprocally scale-free biological networks between disease genes and death genes","authors":"Ju Han Kim","doi":"10.1109/BIBM.2013.6732449","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732449","url":null,"abstract":"Summary form only given. Biological networks often show a scale-free power-law distribution. Furthermore, leathal genes tend to form functional hubs whereas non-leathal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. Here we report two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation-sensitivity (PSN) network. Hubs of both networks demonstrate a low molecular evolutionary rate and a high codon adaptation index, indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes whereas PSN hubs with disease genes and drug targets. PPI network hubs are enriched with essential cellular processes whereas PSN hubs with environmental interactions like TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are reciprocal to each other but work in concert between death and disease.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"134 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86521893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Elucidation of drivers for cancer initiation and metastasis: A data-mining approach 阐明癌症起始和转移的驱动因素:一种数据挖掘方法

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2013-12-01 DOI: 10.1109/BIBM.2013.6732450

Ying Xu

引用次数: 0

The design and implementation of inpatient medical expenses analysis system 住院医疗费用分析系统的设计与实现

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2013-12-01 DOI: 10.1109/BIBM.2013.6732687

Wangbin, Xieqi, Shihuaxin, Caoxinyu, Wangwenjing, Chendi

引用次数: 0

Big data analytics for drug discovery 药物发现的大数据分析

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2013-12-01 DOI: 10.1109/BIBM.2013.6732448

Keith C. C. Chan

{"title":"Big data analytics for drug discovery","authors":"Keith C. C. Chan","doi":"10.1109/BIBM.2013.6732448","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732448","url":null,"abstract":"Big Data refers to data sets that are so large and complex that traditional data processing tools and technologies cannot cope with. The process of examining such data to uncover hidden patterns in them is referred to as Big Data Analytics. Drug discovery is related to big data analytics as the process may require the collection, processing and analysis of extremely large volume of structured and unstructured biomedical data stemming from a wide range of experiments and surveys collected by hospitals, laboratories, pharmaceutical companies or even social media. These data may include sequencing and gene expression data, drug data including molecular data, protein and drug interaction data, clinical trial and electronic patient record data, patient behavior and self-reporting data in social media, regulatory monitoring data, and literatures where trends and drug repurposing and protein-protein interaction data may be found. To analyze such diversity of data types in large volumes for the purpose of drug discovery, we need algorithms that are simple, effective, efficient and scalable. In this talk, we discuss how we can take advantage of the recent development in big data analytics to improve the drug discovery process. We describe what have recently been done and what remain to be done to develop big data algorithms for drug discovery. We present the effort we have recently made to develop such algorithms to uncover hidden patterns in such data as unreported drug side-effect discussions in social media communications, patient record and sequencing data, regulatory monitoring and drug-protein interaction data, protein-chemical interactions data, etc., for drug side-effect prediction and how such predictions may be used to determine possible drug structures with different desirable properties. We discuss how big data analytics may contribute to better drug efficacy and safety for pharmaceutical companies and regulators.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"40 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74053917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Discussion on TCM pulse diagnosis technology evaluation standard 中医脉诊技术评价标准探讨

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2013-01-01 DOI: 10.1109/BIBM.2013.6732671

K. Mi, Yinan Tang, Sili Tan

引用次数: 0

Effect of Tuina therapy and Baduanjin exercise for primary fibromyalgia syndrome: A prospective, randomized study 推拿疗法和八段筋运动治疗原发性纤维肌痛综合征的疗效:一项前瞻性、随机研究

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2013-01-01 DOI: 10.1109/BIBM.2013.6732685

Q. Tian, Rusong Guo, Kiulam Chung, Shan Wu

引用次数: 0